Single-cell CellPhoneDB communication mapping

Overview

Apply this skill when a user wants to quantify ligand–receptor communication between annotated single-cell populations and display the networks with CellChatViz. It distils the workflow from t_cellphonedb.ipynb, which analyses EVT trophoblast data.

Instructions

1. Prepare the environment - Use an environment with omicverse>=0.2, scanpy, anndata, pandas, matplotlib, and cellphonedb resources. The tutorial assumes the pre-built CellPhoneDB v5 SQLite bundle downloaded as cellphonedb.zip in the working directory. - Activate omicverse plotting defaults via ov.plot_set() so that downstream figures follow the project palette.
2. Load and subset the annotated AnnData object - Read the normalised counts with adata = ov.read('data/cpdb/normalised_log_counts.h5ad'). - Filter to the cell populations of interest using adata.obs['cell_labels'] (e.g., EVT, dNK, VCT). Ensure adata.obs['cell_labels'] is categorical and free of missing values so CellPhoneDB groups cells correctly. - Confirm values are log-normalised (adata.X.max() should be <10 and non-integer); raw counts inflate CellPhoneDB permutations.
3. Run CellPhoneDB via omicverse - Execute ov.single.run_cellphonedb_v5 with the curated AnnData and metadata column: python cpdb_results, adata_cpdb = ov.single.run_cellphonedb_v5( adata, cpdb_file_path='./cellphonedb.zip', celltype_key='cell_labels', min_cell_fraction=0.005, min_genes=200, min_cells=3, iterations=1000, threshold=0.1, pvalue=0.05, threads=10, output_dir='./cpdb_results', cleanup_temp=True, ) - Persist the outputs for reuse (ov.utils.save(cpdb_results, ...), adata_cpdb.write(...)). Saving avoids recomputing permutations.
4. Initialise CellChat-style visualisation - Create a colour dictionary that maps ordered cell_labels categories to adata.uns['cell_labels_colors'] from previous plots. - Instantiate the viewer: viz = ov.pl.CellChatViz(adata_cpdb, palette=color_dict). Inspect adata_cpdb to ensure communication slots (uns/obsm) were populated.
5. Summarise global communication - Derive aggregated counts/weights with viz.compute_aggregated_network(pvalue_threshold=0.05, use_means=True). - Plot overall interaction strength and counts using viz.netVisual_circle(...) with matching figure sizes and colormaps. - Generate outgoing/incoming per-celltype circles using viz.netVisual_individual_circle and viz.netVisual_individual_circle_incoming to highlight senders versus receivers.
6. Interrogate specific pathways - Compute pathway summaries: pathway_comm = viz.compute_pathway_communication(method='mean', min_lr_pairs=2, min_expression=0.1). - Identify significant signalling routes with viz.get_significant_pathways_v2(...), then plot selected pathways using viz.netVisual_aggregate(..., layout='circle'), viz.netVisual_chord_cell(...), or viz.netVisual_heatmap_marsilea(...). - For ligand–receptor focus, call viz.netVisual_chord_LR(...) or viz.netAnalysis_contribution(pathway) to surface dominant pairs.
7. System-level visualisations - Compose bubble summaries for multiple pathways with viz.netVisual_bubble_marsilea(...), optionally restricting sources_use/targets_use. - Display gene-level chords via viz.netVisual_chord_gene(...) to inspect signalling directionality. - Evaluate signalling roles using viz.netAnalysis_computeCentrality(), viz.netAnalysis_signalingRole_network_marsilea(...), viz.netAnalysis_signalingRole_scatter(...), and viz.netAnalysis_signalingRole_heatmap(...) for incoming/outgoing programmes.
8. Troubleshooting tips - Metadata alignment: CellPhoneDB requires a categorical celltype_key. If the column contains spaces, mixed casing, or NaN, clean it (adata.obs['cell_labels'] = adata.obs['cell_labels'].astype('category').cat.remove_unused_categories()). - Database bundle: cpdb_file_path must point to a full CellPhoneDB v5 SQLite zip. If omicverse raises FileNotFoundError or missing receptor tables, re-download the bundle from the official release and ensure the zip is not corrupted. - Permutation failures: Low cell counts per group (<min_cells) cause early termination. Increase min_cell_fraction thresholds or merge sparse clusters before rerunning. - Palette mismatches: When colours render incorrectly, rebuild color_dict from adata.uns['cell_labels_colors'] after sorting categories to keep nodes and legends consistent.

Examples

• "Run CellPhoneDB on our trophoblast dataset and export both the cpdb results pickle and processed AnnData."
• "Highlight significant 'Signaling by Fibroblast growth factor' interactions with chord and bubble plots."
• "Generate outgoing versus incoming communication circles to compare dNK subsets."

References

• Tutorial notebook: t_cellphonedb.ipynb
• Example data: omicverse_guide/docs/Tutorials-single/data/cpdb/
• Quick copy/paste commands: reference.md